Questions

  • is it okay to use the mean value across the admin1?

To do

  • Look at each month separately
  • Compute co-occurrence of forecast and observed dry spells instead of observed monthly precip
  • For these compute misses/false alarms with current approach. Plus compute the 50% of members threshold and make a misses/false alarms plot with that.
  • Remove times when there were <=3 admin2’s within the admin1 experiencing a dry spell

Background

This document explores the skill of ECMWF’s seasonal forecast, to predict dry spells. ECMWF releases a forecast each month. This forecast includes projected total precipitation per month for 1 to 6 months ahead. The 1 month ahead is the month the forecast was released, and the release date is always the 13th of the month. I.e. the 1 month leadtime only becomes available when we are alread two weeks into that month.

ECMWF’s forecast is a probabilistic forecast, meaning it consists of several members (=models) each having their projected precipitation. This is what in this document is referred to as % of members, and can be interpreted as a probability of the event occurring.

Definitions

This analysis

  • Only looks at the Southern region since almost all historical dry spells occurred in that region
  • Only looks at the months of December, January, February since these months the crops are most sensitive to dry spells
  • Computes the monthly precipitation as the mean value of all cells within the admin1

What is the percentage of ensemble members forecasted below a certain threshold?

The figure below shows for how many months the % of ensemble members was below 180 mm. The 180 mm was the threshold set based on overlap of dry spells and monthly precipitation analysis.

We can see that the number of months lowers as the percentage increases, which is expected. While there are slight differences between lead times, the pattern is comparable.

To set the threshold of % of members, we look at the observed data and count how many months had <=180mm. This were 21 months. We then choose the % of members for each leadtime which has closest to 21 months forecasted <= 180mm

What is the miss and false alarm rate per leadtime?

After setting the % of members, we get a list of forecasted months for which we would have triggered. Now we can compare these with the observed months with less than 180 mm of precipitation.

Heatmaps (ugly)